Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora

Abstract

Did Ludwig van Beethoven (1770-1827) re-use material when composing his piano sonatas? What repeated patterns are distinctive of Beethoven's piano sonatas compared, say, to those of Frédéric Chopin (1810-1849)? Traditionally, in preparation for essays on topics such as these, music analysts have undertaken inter-opus pattern discovery-informally or systematically-which is the task of identifying two or more related note collections (or phenomena derived from those collections, such as chord sequences) that occur in at least two different movements or pieces of music. More recently, computational methods have emerged for tackling the inter-opus pattern discovery task, but often they make simplifying and problematic assumptions about the nature of music. Thus a gulf exists between the flexibility music analysts employ when considering two note collections to be related, and what algorithmic methods can achieve. By unifying contributions from the two main approaches to computational pattern discovery-viewpoints and the geometric method-via the technique of symbolic fingerprinting, the current chapter seeks to reduce this gulf. Results from six experiments are summarized that investigate questions related to borrowing, resemblance, and distinctiveness across 21 Beethoven piano sonata movements. Among these results, we found 2-3 bars of material that occurred across two sonatas, an andante theme that appears varied in an imitative minuet, patterns with leaps that are distinctive of Beethoven compared to Chopin, and two potentially new examples of what Meyer and Gjerdingen call schemata. The chapter does not solve the problem of inter-opus pattern discovery, but it can act as a platform for research that will further reduce the gap between what music informaticians do, and what musicologists find interesting.

A computational study on outliers in world music

“…Music similarity is studied in several MIR application areas including automatic genre classification [ 32 ], cover song detection [ 33 ], structural segmentation [ 34 ], pattern recognition [ 35 ] and music recommendation [ 36 ]. In the Music Information Retrieval Evaluation eXchange (MIREX), the annual public evaluation of MIR systems and algorithms, there is a task on Audio Music Similarity [ 37 ].…”

Impaired Maintenance of Interpersonal Synchronization in Musical Improvisations of Patients with Borderline Personality Disorder

“…(10) CR_dur stands for compression ratio applied to (ontime, duration)-pairs. Existing work posits that the more it is possible to compress data, the more structure or patterning the original data contains (Collins et al, 2010 , 2016 ; Collins and Meredith, 2013 ). The more rhythmic motifs or patterns in a participant's playing, the more their corresponding (ontime, duration)-point set tends to be compressible, and the higher the compression ratio will be.…”

“…There is a focus on how performers vary in playing the same piece, with less attention paid to what notes are played, since this is the same or very similar across performances. Relative to this literature, the variables we calculate include some novel quantifications of what is being played—of rhythmic motifs/patterns, based on previous investigations into automatic pattern discovery in music (Collins et al, 2010, 2016; Collins and Meredith, 2013). While our hypotheses are concerned mainly with temporal IPS, it could be that aspects of attachment style and impulsivity manifest not so much in timing information as in other dimensions of musical organization.…”

“A Good Algorithm Does Not Steal – It Imitates”: The Originality Report as a Means of Measuring When a Music Generation Algorithm Copies Too Much

“…Based on the features of melodies involved in selected plagiarism cases, Müllensiefen and Pendzich [22] derive an algorithm for predicting the associated court decision, and it identifies the correct outcome with 90% success rate. Recent failed or overturned cases also indicate that while music similarity and circumstantial evidence are necessary for delivering a verdict in favour of plagiarism having occurred, they are not sufficient, in that the distinctiveness of the music with respect to some larger corpus plays an important role too [8,4,24]: melodies that share contours and begin and end on the same scale steps may well point to potential cases of plagiarism, but it is likely that other melodies will have these same characteristics too [24]; drum beats, where the initial space of possibilities is smaller compared to pitched material, have been less successful as bases for music plagiarism convictions [26].…”

“…For instance, "Why do 90% of the pitch-rhythm combinations in bars 1-20 of your piece occur also in this string quartet movement by Haydn?!" The representations and calculations required to reason this way, especially in algorithmic fashion, began in [19] and have been implemented in various forms since [35,1,4]. In the note-counting vein, in Section 3.1 we define a similarity measure based on the P3 algorithm [35].…”